In larger organizations and companies, contents (including legal content) are present in variety of formats, and therefore automatic content management has become a major challenge in such organizations. The transformation of various content formats, including legacy content, by extracting intelligence, enhancing the content to meet market expectations and structuring it, then, is increasingly important.
One challenge with the transformation of the unstructured data is that the level of inconsistency is very high in the unstructured data which makes structuring of such data difficult.
Another problem with such transformation is that the volume of unstructured documents to be transformed to the structured data is roughly in millions. In such case, one cannot rely on the manual process and there is a need of automation in the process of transforming unstructured documents into the structured documents.
Yet another problem with the transformation of unstructured data is that the input content may not be static in nature, which make transformation difficult.
Still another problem with the transformation of the unstructured data is to achieve the transformation within a tight business schedules requirements.
In view of forgoing discussion, there is a need to develop an automated system and method for enriching and transforming unstructured data by extracting intelligence from unstructured data source which can help in tackling large number of documents with high level of consistency on the basis of varying level of business logic which can also identify risks in early life cycle of transformation and can comprehensively identify extraction and transformation requirements of a business.